Control-Oriented Transmission Scheduling for Multiuser WNCSs With Local and Remote Controllers

We investigate a time-sensitive wireless networked control system (WNCS) where multiple Internet of Things (IoT) sensors embedded with their respective local controllers send their observations to a remote controller over shared wireless channels. From an infinite-time horizon perspective, each process should be stabilized essentially to prevent the process’s states from divergence. Nevertheless, limited channel resources may not fulfill users’ stability requirements due to possibly insufficient transmission attempts. Regarding the tradeoff between stability property and channel resources, we aim to design a transmission scheduling policy that minimizes the infinite-time control cost under channel constraints. Starting with the stability condition analysis under varying scheduling policies, the applied decentralized networked control architecture shows its superiority in extending the WNCS’s scale. By approximately expressing control cost as a function of the Age of Information (AoI), the considered scheduling issue is transformed into an AoI-dependent optimization problem under channel and stability constraints. Then, we develop a control-oriented Whittle index policy where AoI, system parameters, and stability incentives construct the Whittle indexes. Numerical results demonstrate that our proposed policy outperforms the baseline policies in terms of control cost, especially in heterogeneous WNCSs. Furthermore, results show that the proposed policy containing stability factor can support more users with respective stability guarantees.

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Publication Details

Journal
IEEE Internet of Things Journal
Published
2025-01-16
DOI
https://doi.org/10.1109/jiot.2025.3526830
Primary Topic
Microgrid Control and Optimization
Type
article
Field-Weighted Citation Impact
0.00

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article

Control-Oriented Transmission Scheduling for Multiuser WNCSs With Local and Remote Controllers

Bin Cao, Ying Wang, Qinyu Zhang, Pengfei Duan et al.
IEEE Internet of Things Journal
Microgrid Control and Optimization
article

Control-Oriented Transmission Scheduling for Multiuser WNCSs With Local and Remote Controllers

Bin Cao, Ying Wang, Qinyu Zhang, Pengfei Duan, Dawei Chen, Shaohua Wu
article en

Abstract

We investigate a time-sensitive wireless networked control system (WNCS) where multiple Internet of Things (IoT) sensors embedded with their respective local controllers send their observations to a remote controller over shared wireless channels. From an infinite-time horizon perspective, each process should be stabilized essentially to prevent the process’s states from divergence. Nevertheless, limited channel resources may not fulfill users’ stability requirements due to possibly insufficient transmission attempts. Regarding the tradeoff between stability property and channel resources, we aim to design a transmission scheduling policy that minimizes the infinite-time control cost under channel constraints. Starting with the stability condition analysis under varying scheduling policies, the applied decentralized networked control architecture shows its superiority in extending the WNCS’s scale. By approximately expressing control cost as a function of the Age of Information (AoI), the considered scheduling issue is transformed into an AoI-dependent optimization problem under channel and stability constraints. Then, we develop a control-oriented Whittle index policy where AoI, system parameters, and stability incentives construct the Whittle indexes. Numerical results demonstrate that our proposed policy outperforms the baseline policies in terms of control cost, especially in heterogeneous WNCSs. Furthermore, results show that the proposed policy containing stability factor can support more users with respective stability guarantees.

IEEE Internet of Things JournalVol. 12(10)
Harbin Institute of Technology (CN), Aeronautical Development Establishment (IN)
National Natural Science Foundation of China
Affordable and clean energy
Openalex Percentile: Top 99%
Microgrid Control and Optimization
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